A Multichannel Seismic Attenuation Compensation Method and Device Based on Structure-Guided Sparse Inversion

Abstract

This invention discloses a multichannel seismic attenuation compensation method and apparatus based on structure-guided sparse inversion. The method includes: obtaining the seismic wavelet and reflection coefficient sequence of the seismic record to be compensated according to a convolutional attenuation model; constructing a preliminary objective function based on the seismic wavelet and reflection coefficient sequence; extracting subsurface structural dip features based on the structural tensor of the seismic record to be compensated; and constructing a structure-guided sparse inversion attenuation compensation objective function based on the preliminary objective function and the dip features, for compensating the seismic record to be compensated. This invention fully utilizes the structural information and sparsity of reflection coefficients in observed seismic records, which helps improve the signal-to-noise ratio and resolution of the compensation results, making the compensation results stable, reliable, more in line with actual conditions, and more conducive to meeting production needs.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration, and in particular to a method and apparatus for multichannel seismic attenuation compensation based on structure-guided sparse inversion. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] When seismic waves propagate through subsurface media, amplitude attenuation and phase velocity dispersion often occur due to the inelasticity of the medium. Therefore, absorption attenuation reduces the resolution and fidelity of seismic data, interfering with the interpretation of migrated seismic data. This attenuation can be described using attenuation models based on the Kramers-Kroning relationship, such as the Kolsky-Futterman model, the power-law model, and the Kjartansson constant Q model (where Q is the quality factor). These models have all been validated through experiments and field data.

[0004] To correct amplitude attenuation and dispersion, and improve seismic fidelity and resolution, a series of seismic attenuation compensation methods have been proposed. Traditional compensation methods include time-varying deconvolution, time-varying spectral whitening, and inverse Q-filtering. Generally, these methods can effectively correct dispersion, but amplitude compensation is challenging due to the presence of high-frequency noise in the original seismic data. Inverse Q-filtering is widely used in seismic attenuation compensation, but as a direct compensation method, it inevitably amplifies the amplitude of high-frequency noise due to the exponential amplification of amplitude, making the amplitude of the compensated seismic record unstable.

[0005] In the past two decades, to overcome the instability of traditional inverse Q-filters, inversion-based attenuation compensation methods have been proposed. These methods construct a matching term for the observed attenuation records using the observed attenuation records and a convolutional attenuation model. First, the subsurface reflection coefficient sequence is reconstructed, and then the wavelet is convolved to obtain the compensated seismic record. Currently, commonly used inversion-based seismic attenuation compensation methods can be mainly divided into two categories: one is the regularization method, which transforms the attenuation compensation problem into a Fredholm integral equation and introduces Tikhonov regularization to improve the stability of the inversion. Taking advantage of the poor performance of inverse Q-filters and the strong noise resistance of sparse reconstruction, an inversion-based L1 norm minimization attenuation compensation method is proposed to improve the stability and resolution of the compensation results. The other method, within a Bayesian framework, uses the Cauchy-Gaussian prior distribution to establish the objective function for attenuation compensation. The least squares method is used to optimize the objective function to obtain stable compensation results. However, these inversion-based attenuation compensation methods all neglect the accuracy and spatial continuity issues of sparse reconstruction of reflection coefficients, making the attenuation compensation results susceptible to high-frequency noise and reducing the signal-to-noise ratio of the compensation results.

[0006] In recent years, significant progress has been made in the research of multichannel seismic inversion algorithms, which can effectively enhance the spatial continuity of inversion results. Traditional multichannel inversion strategies can be mainly divided into two categories: model-driven and data-driven. Model-driven multichannel inversion strategies require prior assumptions that the parameters to be inverted conform to specific spatial continuity characteristics, such as using L2 norm constraints on adjacent seismic traces or total variational constraints. They assume that the inversion parameters conform to pre-defined lateral smoothing or blocky characteristics and then employ a multichannel inversion strategy. Therefore, when the inversion parameters do not meet the pre-defined assumptions, spatial continuity cannot be maintained, and the effective signal will be compromised. To adaptively constrain spatial distribution, many data-driven multichannel inversion methods have been proposed, including directly obtaining the dip angle of the seismic record phase axis through the slope method, obtaining the slope of the seismic record through plane wave decomposition, and determining the structural trend through local seismic record similarity. However, existing multichannel inversion strategies struggle to extract reliable subsurface structural information from attenuated, low signal-to-noise ratio seismic records, and therefore cannot be directly applied to multichannel attenuation compensation of seismic records.

[0007] As can be seen from the above analysis, existing technologies cannot achieve high-precision sparse reconstruction of reflection coefficients and acquisition of prior information on underground structures, and therefore cannot provide more accurate prior information for seismic attenuation compensation. Summary of the Invention

[0008] To address the problems in the prior art, this invention provides a multi-channel seismic attenuation compensation method and apparatus based on structure-guided sparse inversion, offering a superior compensation method to solve the problems of velocity dispersion and amplitude attenuation in the propagation of seismic waves in inelastic media in the prior art.

[0009] This invention provides a multichannel seismic attenuation compensation method based on structure-guided sparse inversion, addressing the technical problem that existing technologies cannot achieve high-precision sparse reconstruction of reflection coefficients and acquisition of prior information on subsurface structures, thus providing more accurate prior information for seismic attenuation compensation. The method includes: describing the propagation law of seismic waves in a viscoelastic medium based on a convolutional attenuation model; employing a seismic record absorption attenuation compensation strategy based on the sparse characteristics of reflection coefficients through sparse inversion and then forward modeling of reflection coefficients; extracting subsurface structural dip features from the structural tensor by observing seismic records with noise and absorption attenuation; and constructing a structure-guided sparse inversion objective function by combining sparse constraints and structural dip features to eliminate the effect of medium absorption attenuation, thereby balancing the shallow and deep energy of the compensated seismic record and improving the resolution and signal-to-noise ratio of the seismic record.

[0010] As one aspect of this invention, a multi-channel seismic attenuation compensation method based on structure-guided sparse inversion is provided, comprising:

[0011] Based on the convolutional attenuation model, the seismic wavelet and reflection coefficient sequence of the earthquake record to be compensated are obtained;

[0012] A preliminary objective function is constructed based on the seismic wavelet and reflection coefficient sequence;

[0013] Extract subsurface tectonic dip features from the structural tensor of the earthquake record to be compensated;

[0014] Based on the preliminary objective function and the dip angle characteristics, a construction-guided sparse inversion attenuation compensation objective function is constructed to compensate the seismic records to be compensated.

[0015] This invention also provides a multichannel seismic attenuation compensation device based on structure-guided sparse inversion, which addresses the limitations of existing technologies in achieving high-precision sparse reconstruction of reflection coefficients and acquisition of prior information on subsurface structures, thereby providing more accurate prior information for seismic attenuation compensation. The device includes: a data acquisition module for obtaining the seismic wavelet and reflection coefficient sequence of the seismic record to be compensated based on a convolutional attenuation model; an initial objective function construction module for constructing a preliminary objective function based on the seismic wavelet and reflection coefficient sequence; a subsurface structure dip angle determination module for determining the dip angle characteristics of the subsurface structure based on the seismic record to be compensated; and a structure-guided sparse reflection coefficient inversion module for constructing a structure-guided sparse inversion attenuation compensation objective function based on the preliminary objective function and the dip angle characteristics, used for compensating the seismic record to be compensated.

[0016] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0017] This invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to perform the above-described method.

[0018] Using embodiments of this invention, a convolutional attenuation forward model is constructed based on the plane wave propagation mechanism and absorption attenuation model. Based on the L1-2 norm regularization assumption of the reflection coefficient sparsity, and using subsurface tectonic information extracted from structural tensors based on observed seismic records, a multi-channel simultaneous seismic compensation regularization term considering spatial continuity is constructed to constrain the sparse reconstruction of the reflection coefficient, obtaining a high-precision, strong spatial continuity, and high signal-to-noise ratio spatial distribution of the reflection coefficient. Finally, the compensation result is determined by using the seismic wavelet with the unattenuated reflection coefficient convolution. By integrating tectonic-guided sparse reconstruction of the reflection coefficient with seismic record attenuation compensation, the advantages of tectonic-guided sparse reconstruction and subsurface tectonic extraction from structural tensors are fully utilized. This provides more accurate prior information for seismic record attenuation compensation, which is beneficial for improving the stability, signal-to-noise ratio, and resolution of the compensation results, making the compensation results more consistent with actual conditions and meeting production needs. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a multi-channel seismic attenuation compensation method based on structure-guided sparse inversion provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a wave impedance model provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of a synthesized noise-free and attenuation-free seismic record provided in an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of a synthesized, noisy, and attenuated seismic observation record provided in an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram illustrating an inversion and direct compensation strategy provided in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of a structural tensor provided in an embodiment of the present invention;

[0026] Figure 7 This is a schematic diagram of the extracted dip angle of underground structures provided in an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of a compensation result provided in an embodiment of the present invention;

[0028] Figure 9 This is a schematic diagram of a noise-free, attenuation-free, locally magnified seismic record provided in an embodiment of the present invention;

[0029] Figure 10 This is a schematic diagram of a locally magnified seismic record of a compensation result provided in an embodiment of the present invention;

[0030] Figure 11 This is a schematic diagram showing the comparison of amplitude spectra before and after compensation, provided in an embodiment of the present invention;

[0031] Figure 12 A schematic diagram of a multichannel seismic attenuation compensation device based on structure-guided sparse inversion provided in an embodiment of the present invention;

[0032] Figure 13 A computer device provided as an embodiment of the present invention.

[0033] [Explanation of Labels in the Attached Image]

[0034] 121. Data Acquisition Module;

[0035] 122. Module for determining the dip angle of underground structures;

[0036] 123. Initial objective function construction module;

[0037] 124. Construct a sparse inversion module for the guiding reflection coefficient;

[0038] 1302. Computer equipment;

[0039] 1304, Processor;

[0040] 1306. Memory;

[0041] 1308. Drive mechanism;

[0042] 1310. Input / output module;

[0043] 1312. Input devices;

[0044] 1314. Output devices;

[0045] 1316. Presentation equipment;

[0046] 1318. Graphical User Interface;

[0047] 1320. Network interface;

[0048] 1322. Communication link;

[0049] 1324. Communication bus. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] This invention provides a multi-channel seismic attenuation compensation method based on structure-guided sparse inversion. Figure 1 This is a flowchart of a multi-channel seismic attenuation compensation method based on structure-guided sparse inversion provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0052] S101, Based on the convolutional attenuation model, the seismic wavelet and reflection coefficient sequence of the earthquake record to be compensated are obtained to describe the propagation law of the seismic wave in the underground medium.

[0053] The expression for the propagation of a plane wave along the z-direction in an attenuating medium can be given by:

[0054] p(z,t)=exp(i[ωt-k(ω)z]), (1)

[0055] Where ω is the angular frequency, k(ω) is the complex wave number, and i is the imaginary unit. The complex wave number is defined as...

[0056]

[0057] In the formula, v(ω) and α(ω) are the velocity and attenuation coefficients related to the angular frequency, respectively. Assuming the attenuation coefficient α(ω) is linearly related to the angular frequency, the attenuation coefficient is expressed as...

[0058]

[0059] Where v(ω0) and Q(ω0) are the velocity and Q value at the reference angular frequency ω0, respectively, and Q is the quality factor, a physical quantity used to quantitatively describe the absorption and attenuation effect of the formation. This invention uses the Kolsky-Futterman model to describe the relationship between seismic wave velocity and angular frequency:

[0060]

[0061] Substituting equations (3) and (4) into equation (2), the complex wavenumber can be expressed as:

[0062]

[0063] Considering the high-frequency approximation assumption of equation (4), the Kolsky-Futterman model is modified to obtain a new wavenumber expression.

[0064]

[0065] In the formula, ωh is the highest angular frequency of the seismic band. For simplification, γ is defined as 1 / (πQ(ω0)). According to the complex wave number expression, the plane wave in equation (1) is expressed as:

[0066]

[0067] Replacing the distance z with the travel time τ, we get

[0068]

[0069] In the derivation, the approximation between Q(ω) and Q(ω0) is based on the weak frequency dependence of Q in the Kolsky-Futterman model at seismic wave frequencies. It can be seen that the propagation of a plane wave in an inelastic medium involves three terms: attenuation-free propagation, velocity dispersion, and amplitude attenuation. Therefore, the product of the latter two terms is defined as the attenuation function a(ω,τ). Based on the convolution model, the frequency domain attenuation forward modeling can be written as...

[0070]

[0071] Where r(τ) represents the reflection coefficient sequence, and s(ω) and w(ω) represent the seismic data and seismic wavelet in the frequency domain. For finite duration and frequency band, the integral is equivalent to the matrix-vector form:

[0072] d = Gr, (10)

[0073] Where d and r are the attenuated seismic data and reflection coefficient sequence, respectively, and G is the attenuated seismic wavelet matrix. Therefore, the attenuated seismic data can be obtained using the convolution model. Considering the high autocorrelation of the attenuated seismic wavelet matrix G, compensation is performed in the time domain. According to formula (10), the propagation law of seismic waves in the subsurface medium can be described using the attenuated seismic wavelet and reflection coefficient sequence, and the synthetic attenuated seismic record can be obtained by forward modeling.

[0074] Based on the constructed convolutional attenuation model, the wave impedance model ( Figure 2 The noise-free, unattenuated seismic record obtained by forward modeling the seismic wavelet convolution is used as a reference record, such as... Figure 3 As shown. By introducing an attenuation factor, performing convolution forward modeling, and adding random noise, we obtain the following... Figure 4 The simulated earthquake record with noise attenuation is shown.

[0075] S102, construct a preliminary objective function based on the seismic wavelet and reflection coefficient sequence.

[0076] Based on the sparse characteristics of the reflection coefficient sequence, a sparse inversion and forward modeling of the reflection coefficient sequence can be used to finally obtain the compensated seismic record after absorption and attenuation.

[0077] Considering that direct seismic record absorption attenuation compensation would produce energy anomalies, based on the sparsity of the subsurface reflection coefficient sequence (i.e., only a few elements of the subsurface reflection coefficient sequence are non-zero) and the convolution forward model (10) constructed by S101, the reflection coefficient sequence r is first inverted, and then the unattenuated seismic wavelet is convolved. The unattenuated seismic wavelet can be directly constructed from the attenuated seismic wavelet. The absorption attenuation compensation of the seismic record is achieved through a two-step method. The absorption attenuation compensation strategy based on reflection coefficient inversion is as follows: Figure 5 As shown, the leftmost curve represents the attenuated seismic data, the middle curve represents the reflection coefficient sequence, and the rightmost curve represents the compensation result. The inversion-based compensation consists of two steps: sparse reflection coefficient inversion and convolution, to eliminate the absorption attenuation effect, where d... c For the compensated seismic data, G c This is an unattenuated seismic wavelet.

[0078] In the first step of reflection coefficient inversion, based on the sparsity of the reflection coefficient sequence, since the traditional L1 norm is a biased approximation of the sparse L0 norm and often cannot provide the sparsest solution, the L1-2 norm sparse inversion method is adopted. Based on the time-domain convolution decay model (10) and the sparsity constraint of the L1-2 norm, the preliminary objective function can be expressed as follows:

[0079]

[0080] Where λ z The parameter α controls the sparsity of the reconstructed reflection coefficient sequence. α is a weighting parameter used to handle ill-conditioned matrices, varying within [0,1]. ||r||1 and ||r||2 represent the L1-norm and L2-norm constraints, respectively. The objective function for retrieving the reflection coefficients consists of two terms: the first is the matching term between the synthetic attenuated seismic record and the seismic record to be compensated, and the second is the sparsity constraint term of the reflection coefficients under the L1-2 norm.

[0081] After obtaining the reflection coefficient sequence through sparse inversion, the reflection coefficients are compared with the unattenuated seismic wavelet G. c Convolution can yield seismic records with absorption and attenuation compensation.

[0082] S103, extract the dip angle features of the subsurface structure based on the structural tensor of the earthquake record to be compensated.

[0083] Since traditional reflection coefficient inversion (11) does not consider the spatial continuity of subsurface structures, this invention uses the structural tensor to extract reliable subsurface structural dip features from attenuated seismic records. The structural tensor is defined as the product of the horizontal and vertical differences of the seismic record. At each sampling point, the structural tensor T is a 2×2 symmetric matrix, represented as follows:

[0084]

[0085] In the formula, g = [g x ,g z ] T ,g x ,g z Let represent the horizontal and vertical gradient components of the seismic record, respectively. To stably extract the dip angle of subsurface structures, a Gaussian filter is used to smooth the structure tensor. Since the eigenvalue decomposition of the structure tensor represents the direction of the phase axis of the seismic record, it can be written as...

[0086] T = a u uu T +a v vv T (13)

[0087] Where a u and a v Let a represent the eigenvalues ​​corresponding to eigenvectors u and v. u ≥a v Since the phase axis of a seismic record can only describe the relative change in wave impedance, such as at model edges, the eigenvalue α... u The larger the value, the more likely the corresponding eigenvector u is to be perpendicular to the reflection interface in the seismic record, and the more likely the eigenvector v is to be parallel to the reflection interface in the seismic record. For a more intuitive understanding of the structure tensor, a schematic diagram is shown below. Figure 6 As shown. The arrows on the right and left represent the structural tensors parallel and perpendicular to the seismic record phase axis (middle black line), respectively. Using the extracted eigenvectors perpendicular to and parallel to the structural direction, the dip angle of the subsurface structure can be calculated.

[0088] tan(θ)=u(1) / v(1), (14)

[0089] Where θ represents the subsurface dip angle, u(1) represents the first element of the eigenvector u, that is, the maximum value among the elements, which represents the main feature of the eigenvector u in the direction, and v(1) represents the first element of the eigenvector v, that is, the maximum value among the elements, which represents the main feature of the eigenvector v in the direction, such as Figure 7 As shown, each sampling point corresponds to the tilt angle value constructed at that point, compared with... Figure 2 and Figure 7As can be seen, the dip angle distribution pattern can basically reflect the underground structure distribution. Therefore, the characteristic vectors u and v obtained by formula (13) can reflect the dip angle characteristics of the underground structure.

[0090] S104. Based on the preliminary objective function and the dip angle characteristics, construct a construction-guided sparse inversion attenuation compensation objective function to perform compensation processing on the seismic record to be compensated.

[0091] By combining sparsity constraints and tectonic dip characteristics, an attenuation compensation objective function for tectonic-guided sparse inversion is constructed to eliminate the effect of medium absorption attenuation, thereby balancing the shallow and deep energy of the absorption attenuation compensated seismic record and improving the resolution and signal-to-noise ratio of the seismic record.

[0092] This invention achieves multi-channel seismic absorption attenuation compensation by stitching together all seismic traces. The single-channel compensation in equation (11) is extended to multi-channel compensation, expressed as follows:

[0093]

[0094] In the formula The diagonal matrix formed by combining the attenuated seismic wavelet matrices G. and These are spliced ​​multichannel reflection coefficient sequences and seismic data, respectively.

[0095] Traditional spatial continuity constraints apply a simple horizontal difference operator.

[0096] D x r = 0, (16)

[0097] To enhance the horizontal continuity of the compensation results, where D x This represents the horizontal difference operator. However, it smooths the tilted interface, blurring the compensated seismic record. After obtaining the eigenvectors, tectonic-guided constraints can be constructed, including smoothing along the seismic record phase axis both parallel and perpendicular. Considering the sparsity of the reflection coefficient sequence, the smoothing regularization term perpendicular to the seismic record phase axis is discarded. The spatial continuity constraint along the parallel tectonic direction is expressed as...

[0098] V T D x r = 0, (17)

[0099] In the formula V T Composed of feature vectors v, it contains the dip angle characteristics of the subsurface structure. Integrating multiple structural guidance constraints (17) into the objective function (15), the attenuation compensation objective function for structural guidance sparse inversion is constructed as follows:

[0100]

[0101] Where λx It is a hyperparameter that controls the strength of the spatial continuity constraint. The attenuation compensation objective function of the structure-guided sparse inversion includes three terms: the first term is the matching term of the forward-modeled synthetic attenuated seismic record and the observed seismic record; the second term is the L1-2 norm sparse constraint term of the reflection coefficient; and the third term is the spatial continuity constraint term constructed through the structural tensor. The attenuation compensation objective function of the structure-guided sparse inversion can be obtained by using the basis pursuit algorithm, the orthogonal matching pursuit algorithm, etc. In this invention, the original problem (18) is first decomposed into two subproblems by the convex difference algorithm (DCA). Then, each subproblem is solved by direct differentiation and the alternating direction multiplier method (ADMM). DCA can effectively handle the difference between the two convexity terms. The attenuation compensation objective function type of the structure-guided sparse inversion is:

[0102]

[0103] in

[0104]

[0105]

[0106] According to DCA, the optimization decomposition of the original construction-guided sparse inversion decay compensation objective function (18) is a two-sequence iterative process:

[0107]

[0108] Where y n Let the subgradient be represented, and expand as follows:

[0109]

[0110] The other subproblem, a traditional L1 norm minimization problem, can be solved directly using ADMM. Its objective function is:

[0111]

[0112] Introducing the auxiliary variable m, the original problem is reorganized as follows:

[0113]

[0114] Using the augmented Lagrange multiplier method, the minimization problem of equation (25) can be rewritten as follows:

[0115]

[0116] Where β is a hyperparameter and к is a Lagrange multiplier. The unconstrained optimization problem (26) can be solved iteratively using ADMM.

[0117]

[0118] Here, operator S represents the soft thresholding function. The final compensation result can be obtained by convolving the reconstructed reflection coefficient sequence with the unattenuated seismic wavelet, as shown below. Figure 8 As shown. To visually compare the effects before and after compensation, local magnifications of the reference seismic record and the compensation results are provided, as shown below. Figure 9 and Figure 10 As shown. In Figure 11 The image shows the original noisy attenuated seismic record, the reference noiseless unattenuated seismic record, and the spectrum of the compensation result. The high-frequency and low-frequency energies of the amplitude spectrum after compensation are both increased, and the amplitude spectrum of the compensation result matches the amplitude spectrum of the reference record well.

[0119] This invention also provides a multi-channel seismic attenuation compensation device based on structure-guided sparse inversion, as described in the following embodiments. Since the principle of this device embodiment in solving the problem is similar to that of the multi-channel seismic attenuation compensation method based on structure-guided sparse inversion, the implementation of this device embodiment can refer to the implementation of the method, and repeated details will not be elaborated further.

[0120] Figure 12 This is a schematic diagram of a multi-channel seismic attenuation compensation device based on structure-guided sparse inversion provided in an embodiment of the present invention, as shown below. Figure 12 As shown, the device includes: a data acquisition module 121, a subsurface structure dip angle determination module 122, an initial objective function construction module 123, and a structure-guided reflection coefficient sparse inversion module 124.

[0121] The data acquisition module 121 is used to obtain the seismic wavelet and reflection coefficient sequence of the seismic record to be compensated according to the convolutional attenuation model. For example, it can obtain the quality factor Q model (externally input or pre-given) describing the attenuation effect and the hyperparameters (which can be selected or pre-set) based on the inversion compensation algorithm to determine the seismic record to be compensated.

[0122] The subsurface structure dip angle determination module 122 is used to determine the subsurface structure dip angle characteristics based on the earthquake record to be compensated; for example, to determine the subsurface structure dip angle characteristics based on the earthquake record to be compensated with noise attenuation.

[0123] The initial objective function construction module 123 is used to construct a preliminary objective function based on the seismic wavelet and reflection coefficient sequence. For example, the first term of the constructed initial objective function is a matching term between the synthetic attenuated seismic record and the seismic record to be compensated, and the second term is a sparse constraint term of the reflection coefficient under the L1-2 norm, expressed as follows:

[0124] A structure-guided reflection coefficient sparse inversion module 124 is used to construct a structure-guided sparse inversion attenuation compensation objective function based on the preliminary objective function and the dip angle characteristics, for compensating the seismic record to be compensated; for example, from the seismic record to be compensated, based on the sparsity characteristics of the reflection coefficient sequence and the extracted subsurface structural dip angle characteristics, the sparse constraint term λ is determined. z and multi-channel space construction constraint term λ x (That is, determine the last two terms in formula (18), determine the spatial distribution of the reflection coefficient of the earthquake record to be compensated (that is, determine r in formula (18); under the convolution model, calculate the compensation result based on the reflection coefficient obtained by multi-channel sparse inversion (that is, obtain the earthquake record after compensation).

[0125] Optionally, the data acquisition module 121 is also used to acquire the quality factor Q model describing the attenuation effect and the hyperparameters based on the inversion compensation algorithm, so as to accurately describe the attenuation effect of seismic waves and the wavelet of seismic records.

[0126] Optionally, the above-mentioned subsurface structure dip angle determination module 122 uses a subsurface structure dip angle extraction algorithm based on structural tensors to obtain subsurface structure dip angle information from the noise-attenuated seismic record to be compensated.

[0127] In an optional embodiment, the structure-guided reflection coefficient sparse inversion module 124 is used to construct an attenuation compensation objective function for structure-guided sparse inversion within the inversion framework, based on the noisy attenuated seismic record to be compensated, the sparsity of the reflection coefficient, and the structure tensor of the extracted tectonic dip information, and to calculate the compensation result.

[0128] As can be seen from the above, in this embodiment of the invention, the seismic record to be compensated is determined by the data acquisition module 121, the quality factor Q model describing the attenuation effect and the hyperparameters based on the inversion compensation algorithm are obtained, the subsurface structural dip angle determination module 122 obtains the subsurface structural dip angle information based on the structural tensor, the preliminary objective function is constructed by the seismic wavelet and the reflection coefficient sequence, and the subsurface reflection coefficient sequence is reconstructed by the construction guided reflection coefficient sparse inversion module 124 under the seismic attenuation compensation framework based on inversion, and finally the reconstructed reflection coefficient sequence is convolved with the unattenuated seismic wavelet to determine the compensation result of the seismic record.

[0129] Through the embodiments of the present invention, the sparse reconstruction of reflection coefficients is integrated with seismic record attenuation compensation. This fully leverages the advantages of the high accuracy of sparse reconstruction of structure-guided reflection coefficients and the stable dip angle of underground structures extracted by structural tensors. This provides more accurate prior information for seismic record attenuation compensation, which helps to improve the stability, signal-to-noise ratio and resolution of the compensation results, making the compensation results more consistent with the actual situation and meeting production needs.

[0130] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the optional or preferred multichannel seismic attenuation compensation methods based on structure-guided sparse inversion described in the above method embodiments.

[0131] This invention also provides a computer-readable storage medium storing a computer program that executes any of the optional or preferred multichannel seismic attenuation compensation methods based on structure-guided sparse inversion described in the above method embodiments.

[0132] In summary, the embodiments of the present invention can achieve, but are not limited to, the following technical effects: (1) The embodiments of the present invention integrate seismic record attenuation compensation into the inversion framework, providing more stable and reliable compensation results, making the compensation independent of seismic wavelets; (2) The method of structural tensor and eigenvalue decomposition is used to extract accurate tectonic dip information from noisy attenuated observation seismic records, providing more accurate prior information; (3) The sparse inversion strategy is used for seismic attenuation compensation, making full use of the sparse characteristics of the reflection coefficient, effectively improving the noise resistance and stability of the compensation results; (4) The embodiments of the present invention comprehensively consider the sparsity of observation records, reflection coefficients and the spatial continuity of underground structures, effectively improving the signal-to-noise ratio and resolution of the compensation results while balancing the energy of seismic records.

[0133] like Figure 13 The illustration shows a computer device provided by an embodiment of the present invention, through which the above-described method of the present invention can be performed. The computer device 1302 may include one or more processors 1304, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 1302 may also include any memory 1306 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the memory 1306 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 1302. In one case, when the processor 1304 executes associated instructions stored in any memory or combination of memories, the computer device 1302 can perform any operation of the associated instructions. The computer device 1302 also includes one or more drive mechanisms 1308 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0134] Computer device 1302 may also include an input / output module 1310 (I / O) for receiving various inputs (via input device 1312) and providing various outputs (via output device 1314). A specific output mechanism may include a presentation device 1316 and an associated graphical user interface (GUI) 1318. In other embodiments, the input / output module 1310 (I / O), input device 1312, and output device 1314 may be omitted, and the device may function solely as a computer device within a network. Computer device 1302 may also include one or more network interfaces 1320 for exchanging data with other devices via one or more communication links 1322. One or more communication buses 1324 couple the components described above together.

[0135] Communication link 1322 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 1322 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0136] This invention also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the method described above.

[0137] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0138] It should also be understood that, in the embodiments of the present invention, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the present invention, the character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0139] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0141] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or may be electrical, mechanical, or other forms of connection.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0143] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A multi-channel seismic attenuation compensation method based on structure-guided sparse inversion, characterized in that, include: Based on the convolutional attenuation model, the seismic wavelet and reflection coefficient sequence of the earthquake record to be compensated are obtained; A preliminary objective function is constructed based on the seismic wavelet and reflection coefficient sequence; Extract subsurface tectonic dip features from the structural tensor of the earthquake record to be compensated; Based on the preliminary objective function and the dip angle characteristics, a construction-guided sparse inversion attenuation compensation objective function is constructed to compensate the seismic records to be compensated. The preliminary objective function constructed based on the seismic wavelet and reflection coefficient sequence further includes, Based on the sparse properties of the reflection coefficient sequence, the reflection coefficient sequence is inverted; The unattenuated seismic wavelet is obtained from the seismic wavelet of the earthquake record to be compensated. The reflection coefficient sequence is convolved with the unattenuated seismic wavelet to form the preliminary objective function. The preliminary objective function is: in, d and r are the seismic record and reflection coefficient sequence to be compensated, respectively, and G is the seismic wavelet matrix of the seismic record to be compensated. λ z It is a hyperparameter that controls the sparsity of the reconstructed reflection coefficient sequence. α It is a weighting parameter used to process ill-conditioned matrices, varying in [0,1], where ||r||1 and ||r||2 represent L1 norm and L2 norm constraints, respectively.

2. The method according to claim 1, characterized in that, Extracting subsurface tectonic dip features based on the structural tensor of the earthquake record to be compensated further includes... The structure tensor T is represented as follows. Formula 13 in, a u and a v These represent the eigenvalues ​​corresponding to eigenvectors u and v. a u ≥ a v ， The eigenvector u is perpendicular to the reflection interface in the seismic record, and the eigenvector v is parallel to the reflection interface in the seismic record. Subsurface structural dip angle θ Represented as, Formula 14 in, This represents the maximum value of the elements in the eigenvector u. This represents the maximum value of the elements in the eigenvector v, and it reflects the main characteristics of the eigenvector v in the direction of the eigenvector v. Formula 14 is derived from Formula 13 and used to reflect the dip angle characteristics of underground structures.

3. The method according to claim 1, characterized in that, Based on the preliminary objective function and the tilt angle characteristics, the attenuation compensation objective function for constructing guided sparse inversion further includes, The single-channel initial objective function in Equation 11 is extended to a multi-channel initial objective function, expressed as follows: Official 15 in, G is a diagonal matrix composed of the seismic wavelet matrices of the earthquake records to be compensated. and These are spliced ​​multichannel reflection coefficient sequences and earthquake records to be compensated, respectively. Spatial continuity constraints along parallel structural directions are expressed as Formula 17 Among them, D x Let V represent the level difference operator, r be the reflection coefficient sequence, and V be the level difference operator. T It consists of feature vector v, which contains the dip angle features of underground structures; By incorporating the multi-channel construction-guided constraint term formula 17 into the initial objective function after multi-channel expansion, the attenuation compensation objective function for construction-guided sparse inversion is constructed as follows: Official 18 in λ x It is a hyperparameter that controls the strength of the continuity constraint in the structural space.

4. The method according to claim 3, characterized in that, The compensation processing of earthquake records to be compensated further includes... Equation 18 is decomposed into two subproblems using the convex difference algorithm; Each subproblem is solved by direct differentiation and alternating direction multiplier method, thereby compensating the seismic record to be compensated.

5. A multi-channel seismic attenuation compensation device based on structure-guided sparse inversion, characterized in that... include, The data acquisition module is used to obtain the seismic wavelet and reflection coefficient sequence of the earthquake record to be compensated based on the convolutional attenuation model. An initial objective function construction module is used to construct a preliminary objective function based on the seismic wavelet and reflection coefficient sequence. The subsurface structure dip angle determination module is used to determine the dip angle characteristics of subsurface structures based on the earthquake records to be compensated. A sparse inversion module for constructing a guided reflection coefficient is used to construct an attenuation compensation objective function for constructing a sparse inversion based on the preliminary objective function and the dip angle characteristics, in order to perform compensation processing on the seismic record to be compensated. The initial objective function construction module is specifically used to invert the reflection coefficient sequence based on the sparsity characteristics of the reflection coefficient sequence; The unattenuated seismic wavelet is obtained from the seismic wavelet of the earthquake record to be compensated. The reflection coefficient sequence is convolved with the unattenuated seismic wavelet to form the preliminary objective function. The preliminary objective function is: Official 11 in, d and r are the seismic record and reflection coefficient sequence to be compensated, respectively, and G is the seismic wavelet matrix of the seismic record to be compensated. λ z It is a hyperparameter that controls the sparsity of the reconstructed reflection coefficient sequence. α It is a weighting parameter used to process ill-conditioned matrices, varying in [0,1], where ||r||1 and ||r||2 represent L1 norm and L2 norm constraints, respectively.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the method of any one of claims 1-4.

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